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18 | IJPR | October-December
International Journal of Pharmaceutical Research
2011, Volume 3, Issue 4, 18-23.
ISSN 0975-2366
Research Article
Process Optimization for Double-Cone Blender and Application of Statistics
Sharma Hitesh*1, Mittal Swati1, Jain Darshana2
1 School of Pharmacy and Technology Management, SVKM’s NMIMS University,
Vile Parle (W), Mumbai – 400 056, India.
2 C U Shah College of Pharmacy, SNDT Women’s University, Santacruz (west), Mumbai 400 049, India.
*Corresponding author: E mail ID: darshanaj_cup@yahoo.com Tel no: +91 9892413756
Received: 23/12/2010, Revised: 01/01/2011, Accepted: 25/02/2011
ABSTRACT
Mixing is probably the most widely performed unit operation in pharmaceutical manufacturing, in fact, it is difficult to
find a product where mixing is not involved in some stage of the process It is difficult to determine, what degree of mixing is
required in particular circumstances and ways to assess the same. Blending operation could be affected by the physical
properties of the materials to be mixed, blending time, rotation speed and percentage of blender capacity. Poor uniformity of
the blend is obtained especially during the mixing of low dose drug with large amount of excipient. Chlorpheniramine
maleate is example of one such drug. Optimization of the mixing procedure in a double cone blender for a potent drug like
Chlorpheniramine maleate was carried out. For uniform mixing of the blend following parameters were optimized blending
time, rotational speed and fill volume. Statistical techniques like Analysis of Variance also known as ANOVA was applied
to designed experiments to determine variations within a batch, within equipment or even due to operators. It proved to be a
valuable tool in maintaining product and process uniformity by comparing two or more groups. This method can be
employed in pharmaceutical industry for optimization of equipments for higher production output and uniform mixing of
low dose drug.
KEYWORDS: Double cone blender, Chlorpheniramine maleate, Optimization, ANOVA.
INTRODUCTION
Mixing is one of the most common pharmaceutical
operations. It is difficult to find a pharmaceutical product in
which mixing is not done at one stage or the other during its
manufacturing [1].Mixing may be defined as the process in
which two or more than two components in a separate or
roughly mixed condition are treated in such a way so that
each particle of any one ingredient lies as nearly as possible
to the adjacent particles of other ingredients or components.
Mixing is a unit operation that involves manipulating a
heterogeneous physical system, with the intent to make it
more homogeneous [2]. The object of mixing operation is
to produce a uniform bulk mixture such that on subdivision
into different doses, each unit dose contains same
proportion of each ingredient. Mixing may be determined
by taking samples from the bulk material and analyzing
them or to initiate or enhance physical or/and chemical
reactions e.g. diffusion, dissolution etc.
Some of the examples of large scale mixing practiced
are:
Mixing of powders in varying proportions prior to
granulation or tabletting
Dry mixing of the materials for direct compression in
tablets
Dry blending of powders in capsules and compound
powders (insufflations).
Blending of powders in cosmetics in the preparation of
face powders, tooth powders [3].
The successful mixing of powder is acknowledged to
be more difficult unit operation because, unlike the situa-
tion with liquid, perfect homogeneity is practically unat-
tainable. In practice, problems also arise because of the
inherent cohesiveness and resistance to movement between
the individual particles. The process is further complicated
in many system, by the presence of substantial segregation
influencing the powder mix. They arise because of differ-
ence in size, shape, and density of the component particles
[4].
In early stages of mixing, generally rate of mixing is
very fast because the mixing particles change their path of
circulation quickly and find themselves in different
environment whereas at the end of the process rate of
mixing reaches to almost zero because the particles do not
find different environment.
Powder mixing is a process in which two or more than
two solid substances are intermingled in a mixer by
continuous movement of the particles. The ease with which
different powders blend to give a homogeneous mixture
varies considerably. It is dependent on various physical
properties of the individual components and on their
relative proportions. It is easier to mix equal weights of two
powders of similar fineness and density than to incorporate
a small proportion of a fine powder in a large mass of a
coarse denser material.
Apart from density and particle size, the stickiness of
the components to be mixed is also important.
1. Prolonged mixing becomes necessary to effectively
distribute materials like lubricants and wetting agents
into tablet granules. However, each process of mixing
has optimum mixing time and so prolonged mixing
may result in an undesired product. So, the optimum
mixing time and mixing speed are to be evaluated [4].
2. A wide difference among properties such as particle
size distribution, shape and surface characteristics
such as surface area and electrostatic charges makes
blending very difficult due to segregation.
3. Flow characteristics such as angle of repose and ability
to flow, abrasiveness of one ingredient upon the other,
state of agglomeration of the ingredients, moisture or
liquid content of the solids, density, viscosity and sur-
Sharma et al / International Journal of Pharmaceutical Research 2011 3(4) 18-23
IJPR | October-December | 19
face tension at operating temperature of any liquid
added, are some other significant considerations in
mixing and selection of mixing equipments. In fact the
properties of blending ingredients dominate the mixing
operation.
It has been generally accepted that in all the mixtures, solid
mixing is achieved by a combination of one or more of the
following mechanisms:
Convective mixing – In convective mixing transfer of
groups of particles takes place from one location to another
by means of blades or paddles of the machine. In the case
of convective mixing material in the mixer is transported
from one location to another. This type of mixing process
will lead to a less ordered state inside the mixer; the com-
ponents which have to be mixed will be distributed over the
other components. With progressing time the mixture will
become more and more randomly ordered. After certain
mixing time the ultimate random state is reached. Usually
this type of mixing is applied for free-flowing and coarse
materials [2].
Shear mixing – In shear mixing, slip planes are set up
within the mass of material.
Diffusive mixing – During this mechanism, mixing occurs
by diffusion process by random movement of particles
within a powder bed and causes them to change their
relative positions.
The theory of powder mixing shows four conditions
that should be observed in the mixing operation. These
conditions are as follows Mixer volume, mixing mechanism
and Mixing time. Physical properties of the material which
affects mixing are Material density, Particle size, and
Particle shape. The chemical and physical state of the
components in the powder will influence the cohesive
nature, stickiness and caking characteristics of the powder,
which will influence its flow characteristics [5].
In pharmaceutical production when the formulation
contains an active ingredient, which is toxic or is present in
a concentration of about 0.5% of the total mass then the
mixing of solids becomes a critically important operation.
Product with too low an active ingredient will be
ineffective and a product with too high active ingredient
may be lethal. To assess uniform mixing of the potent drug
with excipients, cholrpheniramine maleate was selected.
Chlorpheniramine is an antihistamine used to relieve symp-
toms of allergy, hay fever, and the common cold [6-8].
These symptoms include rash, watery eyes, itchy
eyes/nose/throat/skin, cough, runny nose, and sneezing.
This medication works by blocking a certain natural sub-
stance (histamine) that your body makes during an allergic
reaction. By blocking another natural substance made by
your body (acetylcholine), it helps dry up some body fluids
to relieve symptoms such as watery eyes and runny nose
[9].
Mixing of such low dose drug can be achieved by
feeding materials simultaneously to a mill, such as ball
mill, if both require grinding. A wide variety of equipment
is used in different industries. In some machines the
container rotates or in others a device rotates within a
stationary container or in some cases combination of both.
Sometimes baffles or blades are present in the mixer. For
fine, dry powders the use of a screw conveyor often gives
satisfactory mixing while transporting the material and
hence no additional equipment or power is needed for
mixing [10]. A double cone mixer consists of a vessel with
two cones base to base, with or without a cylindrical
section in between as shown in figure 1.
Double cone blender is an efficient mixer for mixing
dry powder and granulates homogeneously due to the
following features.
The mixing barrel and blades are made up of stainless
steel, always keeping clean and away from dirt. The
mixing barrel can be tilted freely at the angle of
0°~360° degrees for discharging and cleaning purpose.
The conical shape at both ends enables uniform
mixing and easy discharge.
The cone is statically balanced to avoid any excessive
load on the gear box and motor.
While the powder can be loaded into the cone through
a wider opening, it can be discharged through a side
valve.
Depending upon the product, paddle types baffles can
be provided on the shaft for better mixing
Interpretation of the results for the experiments that
are carried out should be done on the basis of FDA
guidelines [11]. Statistics is a useful tool in the design,
analysis and interpretation of experiments. It is not feasible
to report every single observation or analytical result. Once
the required observations and measurements have been
completed, it is necessary to assemble and summarize the
data, analyze the significance of any differences between
dosage forms or other variables studied and derive
conclusions [12]. A natural approach would be to extend to
mixers the methods typically used for batch processes,
where a mixing index (typically, a Relative Standard
Deviation, also known as the Coefficient of Variability) is
computed at the “end” of the blending process based on
samples extracted with a thief. Several other indexes have
been used to quantify the mixing performance of particle
processes, for example, Lacey (1943) [13] developed a
mixing index that considers several variances.
Approximately thirty-five other mixing indices can be
found in the excellent review by Fan and coworkers [14],
which outlines the criteria for selecting an index based on
the different degrees of content uniformity that can be
achieved.
These measurements have been applied to many
systems, including various rotating horizontal cylinders
[15], V-blenders [16–20], double cones [21], bin blenders
[22–24], ribbon blenders [25], and continuous blenders [26,
27].
Although indices can be used to quantify whether
design and operating parameters and/or material properties
affect mixing performance, by themselves they are poor
tools when it comes to revealing which effects are more
influential. For the typical number of samples used to
characterize batch processes, RSDs are very “noisy”, and
statistically significant differences between process
responses for different parametric settings can be
established only rarely. Rollins and coworker [28]
presented a theoretical discussion on the advantage of the
ANOVA technique for Monte Carlo simulations. Analysis
of Variance also known as ANOVA is a general method of
analyzing data from the designed experiments, whose
objective is to compare two or more groups[29]. ANOVA
can be used to determine extent of mixing, variations within
a batch, within equipment or even due to operators and
Sharma et al / International Journal of Pharmaceutical Research 2011 3(4) 18-23
20 | IJPR | October-December
hence it is a valuable tool in maintaining product and
process uniformity. It is also used to estimate the variability
and to test for homogeneity of sample averages from
different parts of the blender at different time points.
Materials and equipments
Chlorpheniramine Maleate BP, Supriya Life Science,
Mumbai, Lactose Monohydrate Lactose India Limited,
Aerosil (Colloidal Silicon Dioxide) Merck Limited,
Neelicol Ponceau 4R Neelikon Food Dyes & Chemicals
Ltd were purchased.
Equipment Double cone Blender (15 liters), Gansons
Blender Specification
Capacity: 15liters,
Working Capacity: 12 liters,
Make: SS31
Experimental design
Standardization of Drug
The drug sample was standardized using UV
spectroscopy. U V analytical method was developed for
estimation of Chlorpheniramine maleate. The standard
curve (figure 2) for Chlorpheniramine maleate was
prepared in water using the following method: 100 mg of
Chlorpheniramine maleate was dissolved in 100 ml of
water (Degassed), stirred for 15 minutes followed by
sonication for 15 minutes. It was filtered through Whatman
filter paper to prepare a solution having concentration of 1
mg/ml and this solution was serially diluted to get a range
of concentration from 10 to100 μg/ml. Absorbances of
these solutions were recorded at λmax against appropriate
blank on UV spectrophotometer. The developed method
was validated for parameters like accuracy, precision,
LLOD (Lower Limit of detection) and LLOQ (Lower limit
of quantitation) [30,31].
General procedure for preparation of blend
Blend was prepared using lactose as diluent and
aerosil as glidant with API/color. The concentrations
selected for the excipients are as follows: lactose(98%),
Aerosil(1%) and API (1%) or Colour (1%) was added for
visual observation of uniform mixing of the blend.
Drug/colour was co-sifted with part of diluent through
80# SS sieve (180μ) and mixed in geometric
proportion.
1. Above mixture was added to blender with remaining
part of lactose and aerosil.
2. Blender was operated at various speeds.
3. Stratified sampling was conducted at various time
intervals.
4. Samples collected were analyzed using UV spectros-
copy.
Optimisation of the mixing parameters using ANOVA
statistical technique
Strategy 1: Determination of optimum speed of rotation
Blends were prepared using colorant instead of drug to
optimize speed of rotation for double cone blender
(Gansons). For determining the optimum speed blends were
prepared at 5, 10, and 20 rate per min (RPM) for 5, 10, 15,
20, and 30 min. Visual inspection for colour distribution
was done to determine the optimum speed of rotation.
Strategy 2: Determination of effect of fill volume
Blends of the drug were prepared using blender operated at
20 RPM for 5, 10, 15, 20, and 30 min. The fill volumes
kept were 40%, 60 %and 80%. Triplicate samples were
collected from 6 stratified sampling points of each batch of
the blend. The collected samples were diluted using water
and evaluated using UV spectrophotometer at λmax of
262nm. The raw data obtained was processed to get group
mean, standard deviation and relative standard deviation
and to demonstrate the variability between the samples;
one-way ANOVA was employed using PRISM software
[32, 33].
Determination of powder characteristics for final blend:
[34]
Density
Density influences compressibility, tablet porosity,
dissolution and other properties in a formulation. It is also
important during mixing since mixing of substances of
different density leads to demixing.
Bulk density (ρb)
It is a measure used to describe packing of particles or
granules. It was determined using the formula
Where,
m= Weight of sample taken, vb= Bulk volume
Tapped density (ρt)
For determining the Tapped density measuring
cylinder was filled with known weight of the sample.
Volume of the filled sample was recorded, sample was
mechanically tapped on device. After 50 taps the volume
was again measured and tapped density is calculated using
the equation
Where, m= Weight of sample taken, vt= Tapped volume
Table 1: Mean, S.D and RSD for batches with 40%, 60% and 80% fill volume of the blender.
Time
(mins)
Fill Volume of the Blender
40% Fill Volume of the Blender
60% Fill Volume of the Blender
80%
Mean S.D (+) R.S.D
(%) Mean S.D (+) R.S.D
(%) Mean S.D (+) R.S.D
(%)
5 84.07 6.95 8.27 83.45 20.06 24.04 101.93 7.64 7.49
10 82.35 8.11 9.85 83.51 6.74 8.07 102.20 4.86 4.76
15 92.55 1.12 1.21 95.89 2.20 2.29 100.30 3.89 3.88
20 95.24 3.30 3.47 96.23 2.52 2.62 - - -
30 75.87 11.53 15.20 75.60 14.26 18.86 - - -
Sharma et al / International Journal of Pharmaceutical Research 2011 3(4) 18-23
IJPR | October-December | 21
Compressibility Index (CI)
Compressibility Index can be calculated using below
given equation
Hausner’s Ratio
It is the ratio of bulk volume to tapped volume or
tapped density to bulk density.
Flow properties
Flow properties for a material result from many forces.
There are many types of forces that can act between solid
particles: frictional forces, surface tension forces,
mechanical forces caused by interlocking of particles of
irregular shapes, electrostatic forces and cohesive or
Vander Waals forces. These forces can affect efficient
mixing of powders. The two methods for determining the
flow properties are angle of repose or hopper flow rate
measurements.
Angle of repose (Tan θ)
Angle of repose is the tan inverse of angle between
height (h) of pile of powder and the radius (r) of the base of
conical pile. It was calculated by the below given equation
RESULTS AND DISCUSSION
Standardization of Drug
λmax of Chlorpheniramine Maleate in distilled water
was found to be 262 nm. The standard curve of the drug
was prepared in water (Figure 2). Beer-Lambert’s law was
obeyed over the range and data was found to fit the
equation:
Where, x = concentration in µg/ml and y =
Absorbance
LLOD of the Chlorpheniramine Maleate was found to
be 2 µg/ml and LLOQ for the drug was found to be 6
µg/ml. Thus the standard curve prepared showing good
linearity.
Optimization of the mixing parameters using ANOVA
statistical technique [35].
Mixing apparatus or technique can be optimized easily
in practice by dispersing a highly colored material in a
white diluent. So a highly colored dye was mixed with
lactose which could be visually analyzed. A dilution of
small amount of a potent white medicament (CPM) in a
white diluent will require similar treatment but to be
analyzed by U V Spectroscopy. Samples were taken from 6
different parts of the blender depending on geometric and
potential trouble spots. Here diagonally opposite points and
centre of the mixture were selected as sampling points.
Strategy 1: Optimization of Speed of Rotation
Colored blend was prepared at 5, 10 and 20 RPM
which gave idea about optimization of rotation speed. The
collected stratified samples were weighed (100 mg) and
diluted up to 100 ml. Samples were visually inspected for
difference in color intensity. With the colour blend, it was
seen that at RPM 5 and 10 non uniform colour mixture was
obtained. However with speed 20, initially 15 mins, there
was no uniform mixing, but after 20 mins uniform blend
was obtained. Hence it was decided to make further blends
at a speed of 20 RPM on blender.
Strategy 2: Optimization of Rotation time of blender
Blend with API was prepared at 20 RPM speed and
sampling was done at 5, 10, 15, 20 and 30 min. Three
blends were prepared keeping the fill volume of 40, 60 and
80 % of the total volume of the double-cone blender.
Triplicate samples were collected from any 6 stratified
sampling points of each batch of the blend. The collected
samples were diluted using water and evaluated using UV
spectrophotometer at λmax 262nm.
Average of the drug content at different time point was
determined. The standard deviation, coefficient of
correlation and analysis of variance techniques were used to
check the extent of mixing. The results from the analysis
are given in Table 1.
Table 2: Statistical Application to the blend with 40%,
60% and 80% fill volume
Source of
Variation d.f. Sum of
Squares Mean
Square F
Between 4 1416 354 6.936
Within 25 1276 51.04
Total 29 2692
One Way ANOVA Comparing Results Within 40%Fill
Batch,
F value= 2.76
Since the calculated F value is greater than tabular value we
reject the Null Hypothesis
i.e Significant difference between the samples at different
time intervals
Source of
Variation d.f. Sum of
Squares Mean
Square F
Between 4 1837 459.3 3.559
Within 25 3226 129.1
Total 29 5063
One Way ANOVA Comparing Results Within 60%Fill
Batch,
F value= 2.76
Since the calculated F value is greater than tabular value we
reject the Null Hypothesis
i.e Significant difference between the samples at different
time intervals
Source of
Variation d.f. Sum of
Squares Mean
Square F
Between 2 22.58 11.49 0.3481
Within 21 680.9 32.43
Total 23 703.5
One Way ANOVA Comparing Results Within 80%Fill
Batch,
F value= 3.47
Since the calculated f value is less than tabular value we
accept the null hypothesis
i.e No significant difference between the samples at different
time intervals
Sharma et al / International Journal of Pharmaceutical Research 2011 3(4) 18-23
22 | IJPR | October-December
RSD for samples collected at 15 and 20 min was less
than 4.00 for blends with 40% and 60% fill volume. With
80% of fill volume, uniform mixing is obtained in 15 min.
At other time points and speed the relative standard
deviation was more than 4.00, probably due to cohesiveness
or segregation. However batches collected at 15 and 20 min
are readily passable according to FDA guidelines. ANOVA
was applied to these batches.
Results for the one way ANOVA were as follows
The above observation was supported by F values
calculated using ANOVA method (Table2). Incomplete
mixing at 5 and 10 min and segregation at 30 min mixing
time lead to high S.D and R.S.D values. This suggests that
minimum mixing time required is 20 min. None of the F
values for batches with different fill volume were
significant (Table 3) suggesting that fill volume has no
significant results on the uniformity of the blend.
Determination of powder characteristics for final blend:
[34]
Tapped and bulk density for the powder mixture was
determined and the values for the same were calculated
using the equations. They were found to be 0.769 mg/mL
and 0.755 mg/mL for the final blend. Powders exhibiting
Hausner’s ratio of 1.2 – 1.3 have excellent flow properties.
Hausner ratio for the final blend was found to be 1.385,
hence has excellent flow properties. Compressibility is
indirectly related to the relative flow rate, cohesiveness and
particle size distribution of the powder. Powders with
compressibility values lesser than about 20% have been
found to exhibit good flow properties. Tapped (ρt) and
Apparent Bulk density (ρb) measurements can be used to
estimate the compressibility of a material. Final blend had
compressibility index of 17.77%, indicating excellent flow
properties. This would help in easy flow of the blend from
the hopper and easy punching of tablets or filling of
capsule. Values for angle of repose less than or equal to 30
degrees suggest a free flowing material and angles greater
than or equal to 40 degrees suggest a poorly flowing
material. Angle of repose for the blend was found to be
32.57% thus complying with other results and indicating
good flow properties. The flow property of the blend after
mixing on double cone blender at optimum speed and
optimum time was found to be excellent.
CONCLUSION
The process of mixing through double cone blender
was developed and the process was optimized using the
statistical technique like ANOVA. This procedure can be
employed at industry scale for easy optimization of the
mixing procedure using double cone blender. Statistical
method, ANOVA can be employed similarly to other
equipments as well for easy and rapid optimization of
process.
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*ns: Non significant
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